Tips For Building A Data-Driven Marketing Organization

With over a decade now in the digital optimization space, from tech vendor to digital agency to brand side, I have seen organizations evolve by taking on a more data-driven mindset.

Being data driven is certainly a hot topic in a an era characterized often by Big Data and digital. There is clear progress, in terms of growing sophistication and knowledge, in the digital testing and analytics spaces.

Yet being truly data-driven often requires a full-blown culture change. Having a few people, or even a small army of data scientists won’t cut it. In fact, simply creating these roles without any further effort, I would argue, actually sets organizations back in the sense that it gives organizations the impression – internally and externally – that they are data-driven when, in reality, they’re not. The false hope leads to stagnation in what could otherwise be further advances towards a true data-driven culture.

Yes, having testing and analytics experts in a digital marketing world is definitely a step in the right direction. Some testing is better than no testing. Some analysis is better than no analysis. But why is it so important to take it further? What are those steps?

To understand the answers, let’s first dissect the very simple term data-driven. There are just two words. Let’s face it, data gets a lot of the limelight. Big Data. It’s all in the data. But what gets glossed over way too often is the “driven” piece. Yes, the data must exist to be data-driven, but if there is no action taken, there really isn’t anything data-driven about it.

It is important to take the steps necessary to create an organization where the data doesn’t just exist, but where the data can be transformed into action.

If a small testing group is uncovering insights, but the recommended actions can’t be implemented because there are no cycles in other parts of the organization to account for the time needed to act on testing and analytics data, then you’re stuck in a situation where you are simply “data” – but not data-driven.

If an organization truly wants to be data-driven, you must do much more than hire a few data geeks. In short, a culture needs to permeate across the board. Installing a culture of any kind isn’t easy work. Here are for ideas on how to get started – kicking off with the most difficult.

Check egos at the door

Testing, analytics, and data-driven work are all many things. But in the end, they are largely about finding ways to get better. For a testing and data-driven culture to thrive – and drive success – it must start with a prevailing attitude that whatever it is we do as individuals in an organization, it can be done better.

Egos, however, aren’t fleece jackets that can be easily stowed in the overhead bin. They’re personal and complex. Yet organizations that continuously stress the importance of testing, innovation, and, most specifically, not being afraid to fail go a long way to bringing out the best in us.

The very culture of experimentation can help us all shift from a protective mindset to one where we are more comfortable kicking the tires – even our own tires.

Include optimization time in standard operational work

This can be easy to overlook – which is why most organizations don’t do it. True testing cultures build development time and resources into standard development cycles so that:

New concepts can be built in order for them to be tested in the first place

Insights that come from testing or analytics data can be acted upon and implemented so that the default digital experiences are updated accordingly

New baselines can regularly be updated, setting the stage for continuous improvement

Write copy and design with testing in mind, out of the gate

Standard operational work doesn’t only involve development work. It also means that the people who are paid to write copy, craft messaging, and design components should all be thinking about testing as a resource and practice so they can master their own craft with the help of hard data.

This means drumming up multiple variations – put one version in market out of the gate, but stow the others nearby for future A/B testing. This enables the organization to test nimbly over time.

Alternatively, put these resources through some cycles in an agile process to think about alternatives to testing against the original work.

Again, doing more thinking and creative work upfront doesn’t always seem logical, particularly when time is tight. But the chances of testing new concepts plummet if this thinking is not happening throughout the content-creation process.

Train, enable, evangelize

The data-driven mindset must permeate throughout every corner of the organization. Develop communities with representation from disparate functional and geographic groups across your organization. Create monthly round tables, for example, that provide:

Training sessions

An opportunity for colleagues to learn from one another

People with new skills that they can impart to each other

Planting data-driven seeds so that the expertise and general mentality can sprout throughout

Communities to help spread the data-driven mindset and importance of testing are especially key in large organizations where the impact of a few individuals is greatly limited.

In summary, when we talk about a culture, we’re talking about themes and attributes that permeate beyond a core few. Infusing testing and data-driven concepts into the work of those who don’t call testing their specialty is where and when a data-driven culture begins to take hold.

Make data – be it quantitative analytics data or qualitative customer feedback – be at the heart of every marketing decision. Do that, and you’re on your way.

The next generation of Big Data technology will lead to insights and correlations that reveal new strategies – even new business models. See Data Lakes: Deep Insights.

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About Jake Sapirstein

Jake Sapirstein is the Senior Director of Digital Marketing and Head of Innovation Lab at SAP. Launched in 2011 as SAP Test Lab, the program has become a comprehensive testing and optimization engine that uncovers and communicates valuable insights. In 2014, the program has evolved into Innovation Lab, where it additionally serves as a platform to test and validate innovation projects.